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Data Quality

Data Quality. Class 2 David Loshin. Goals. Cost of low data quality Mapping the information chain Data Quality impacts Economic measures Impact domains Building the Data Quality ROI Model. Goals 2. Data Cleansing Project Goal of the application Components of the application.

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Data Quality

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  1. Data Quality Class 2 David Loshin

  2. Goals • Cost of low data quality • Mapping the information chain • Data Quality impacts • Economic measures • Impact domains • Building the Data Quality ROI Model

  3. Goals 2 • Data Cleansing Project • Goal of the application • Components of the application

  4. Cost of Low Data Quality • Data quality is measured using anecdotes • “Hazy” feeling of wrongness • Desire to gauge the true cost of poor data quality

  5. 5 Steps • Map the Information Chain • Categorize costs associated with low data quality • Identify and estimate actual effect • Determine cost of fixing problem • Calculate Return on Investment (ROI)

  6. Evidence of Economic Impact • Frequent service interruptions and system failures • Drop in productivity vs. volume • High employee turnover • High new business/continued business ratio • Increased customer service requirements • Customer Attrition

  7. The Information Chain • Data flow model • Processing stages • Communication/data transfer

  8. Data Supply Data Acquisition Data Creation Data Processing Data Packaging Decision Making Decision Implementation Data Delivery Data Consumption The Information Chain 2

  9. Information Chain 3 • Information chain = data flow graph • Processing stages are vertices in graph • Directed message-passing channels = directed edges • Examples

  10. Impacts of Low Data Quality • Hard impacts: can be estimated and/or measured • Soft impacts: hard to measure, but definitely are evident

  11. Hard Impacts • Customer attrition • Costs attributed to error detection • Costs attributed to error rework • Costs attributed to prevention of errors • Costs associated with customer service • Costs associated with fixing customer problems • Costs associated with enterprisewide data inconsistency • Costs attributable to delays in processing

  12. Soft Impacts • Difficulty in decision making • Time delays in operation • Organizational mistrust • Lowered ability to effectively compete • Data ownership conflicts • Lowered employee satisfaction

  13. Economic Measures • Cost Increase • Revenue Decrease • Cost Decrease • Revenue Increase • Delay • Speedup • Increase Satisfaction • Decrease Satisfaction

  14. Impact Domains • Operational • Tactical/Strategic

  15. Detection Correction Rollback Rework Prevention Warranty Reduction Attrition Blockading. Operational Impacts

  16. Delays Preemption Idling Increased Difficulty Lost opportunities Organizational mistrust Alignment Acquisition overhead Decay Infrastructure Tactical/Strategic Impacts

  17. Putting it Together • Map the information chain • Conduct interviews to locate data quality problems • Annotate information chain with location of data qualty problems • Identify impact domains for each problem • Characterize economic impact (=cost!) • Aggregate totals

  18. ROI Model • Create a spreadsheet with assigned costs • Add in costs of improvements • Determine best return on investment

  19. Data Cleansing Project • Write an application to cleanse data • Record Parsing • Metadata cleansing • Data standardization • Data correction • Data enhancement

  20. Record Parsing • Data element types • first names • last names • honorifics • titles • street names • directions • business words • etc.

  21. Data Domains • Data types • Subclassed data types = domains • Mappings between domains

  22. Data Domains 2 • Data type = char(2) • 676 possible non-punctuation members • Data Domain: US State abbreviations • 62 possible members • Subclassed data domain: “New England” • {“ME”, “NH”, “VT”, “MA”, “CT”, “RI”}

  23. Data Domains 3 • Enumerated domains • All values are explicit • Rule-based domains • Domain definition is generative

  24. Record Parsing • Tokenizing data elements within an attribute • Assign meaning to tokens • Domain membership • Patterns • Context

  25. Tokenizing • Straightforward • white-space separated • punctuation – important or not? • Result: stream of tokens

  26. Domain Membership • Can each token be assigned to a domain? • Based strictly on token value • Based on patterns • Based on context

  27. Domain Membership 2 • Domains can be maintained in memory using hash tables • Search for domain membership is the same as hash table lookups • What if a token belongs to more than one domain?

  28. Patterns • Certain kinds of data attributes are organized around token patterns • Example: names can appear using these kinds of patterns: • (title) (first) (middle) (last) • (title) (first) (initial) (last) • (first) (middle) (last) • (last) (comma) {first) (middle) • etc.

  29. Context • What happens when a token belongs to more than one domain? • We can use context to infer decision • Build weights based on frequency = training

  30. Next Week • Dimensions of Data Quality • Project specification

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